About me

• M.Sc. in Psychology @ University of Catania
• Research internship in Psychological Methods @ University of Amsterdam
• Ph.D. in Methodology and Statistics @ Tilburg University
• People Data Scientist @ Rabobank

About Rabobank

Data Science definition

Data science is a “concept to unify statistics, data analysis, informatics, and their related methods” to “understand and analyze actual phenomena” with data. It uses techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, information science, and domain knowledge (Wikipedia, 2023)

Data Science

In a Nutshell..

In a Nutshell..

People Analytics (or People Data Science)

• “The analysis of employee and workforce data to reveal insights and provide recommendations to improve business outcomes” (Ferrer and Green, 2021)

• “The organizational function within which data collection, analyses, and translation occur as well as a set of practices that draw on employee data to inform and aid decision-making processes and employee activity throughout the organization” (Polzer, 2022)

People Analytics

In a Nutshell..

In a Nutshell..

Why People Analytics

People Analytics provides tools, methodologies, and techniques to extract meaning out of employee data and make this data useful to:

• Interpret of a large volume of data;
• Identify trends and patterns in employee data;
• Help to predict organizations’ and employees’ needs;
• Prioritize HR activities based on impact utility and return on investment;
• Reduce subjectivity and make decision-making transparent.

Business Problem Discovery

In this phase, there are a few primary goals:

• Identify Problem
• Define objectives and success metrics
• Determine data sources and team

1) Identify problem

• What is the problem?
• Is it a problem? What’s the business value?
• Who are the stakeholders involved?
• What is the scope of the problem?
• What is the problem time frame?

2) Define objectives

We normally try to answer these types of questions with People Analytics:

• How much? (regression)
• Which category? (classification)
• Which group? (clustering)
• Which option should be taken? (recommendation system)

2) Define success metrics

• Specific
• Measurable
• Achievable
• Relevant
• Time-bound

3) Determine data sources and team

To ensure completeness of information and responsibilities, it is essential to:

• Gather information about existing:
  • available data
  • reports and previous projects
  • documentation
• Clarify roles and responsibilities

Example

The IT Department has been a critical part of the organization, driving innovation and ensuring systems run smoothly. However, we’ve noticed an alarming increase in stress-related sick leaves (~ 15%) among IT employees in the last year, which is higher than industry standards (7%). This not only affects their well-being but also disrupts our operations. To address this issue, we aim to develop a stress-risk classification system to identify high-stress cases early and implement targeted interventions. Our goal is to create a healthier work environment for our IT team.

Exercise

Select one of the business cases available on CANVAS and a group of students to work with.

Throughout this lecture, you and your group will work as People Analytic experts and try to go through each of the PA lifecycle steps.

Exercise

For this step, discuss with others in your group how to define the following aspects based on the case description:

• Identify problem
  • Problem
  • Problem size
  • Stakeholders
  • Scope
• Define objective
• Hypothesize Success Metrics

Data Acquisition

Data selection refers to collecting, retrieving, gathering, and sourcing data.

Exercise

Discuss, within your group, what type of data you would like to use for your project. The data can also be other than the types we just discussed. Elaborate on your choice and explain:

• Advantages of using the (combination of) data you proposed
• Disadvantages of using the (combination of) data you proposed

Data Quality issues

Selecting the data that we deem appropriate does not, in itself, guarantee good quality.

Data may be compromised before (or after) acquisition. Typical data quality issues are often due to the following:

• Incompleteness: missing values or lacking certain attributes.
• Noisiness: Recording errors or outliers;
• Inconsistencies: Conflicting records or discrepancies;

Data pre-processing

To ensure good quality levels, it is essential to:

• Conduct data health screens
• Pre-process data

Data Visualization

Data Visualization can also help in understanding the data and getting quick insights

Exercise

Based on the data you decided to use in your project, discuss within your group:

• Potential data quality issues
• Data health screens you would conduct
• Data pre-processing steps
• Visualization/Statistics you would investigate

People Analytics maturity model

People Analytics maturity model (actually)

Descriptive Analytics

Predict-ish Analytics

Predictive Analytics

Prescriptive Analytics

Exercies

Discuss within your group what type of analysis you would conduct for your case study. Specifically, focus on the following aspects:

• How will your data analysis plan contribute to the project’s success?
• Does it match the objective you defined earlier?
• What kind of metric would you use in your analyses (e.g., mean, p-value), and why?
• In what PA maturity stage (e.g., predictive analytics) would your proposed analysis fall?

Interpretation and storytelling

• The pyramid principle
• Data Visualization

The Pyramid Principle

The Pyramid principle technique has been developed by McKinsey & Company and, successively published by Barbara Minto (Minto, 2008, 3rd edition).

This method helps in structuring thinking and convincing audience. The aim, ultimately, is to improve the impact of a project significantly.

The pyramid principle

The Pyramid Principle

flowchart TD
    O[Business problem] --> P[Situation, Complication, Question]
    P --> E(Analyses)
    E --> A
    A[Main message] --> |why| B[Main Argument 1]
    A --> |why| C[Main Argument 2]
    A --> |how| D[Main Argument 3]

    B --> b1[ev. 1]
    B --> b2[ev. 2]
    B --> b3[ev. 3]

    C --> c1[ev. 4]
    C --> c2[ev. 5]
    C --> C3[ev. 6]

    D --> d1[ev. 7]
    D --> d2[ev. 8]
    D --> d3[ev. 9]

A Well-being example

flowchart TD
    O[Business problem] --> P[Reduce sick leave]
    P --> E(Analyses)
    E --> A
    A[Promote Wellbeing] --> |why| B[Improve Employee health]
    A --> |why| C[Enhances work-life balance]
    A --> |how| D[Foster Positive work environment]

    B --> b1[ev. 1]
    B --> b2[ev. 2]
    B --> b3[ev. 3]

    C --> c1[ev. 4]
    C --> c2[ev. 5]
    C --> C3[ev. 6]

    D --> d1[ev. 7]
    D --> d2[ev. 8]
    D --> d3[ev. 9]

Data Visualization principles

• Define your message
• Understand analyses behind the message
• Pick a suitable graph
• Check the graph for clarity
  • formulate message
  • less is more
  • guide attention

Data Visualization charts

Implementation and feedback

• Data and Results are only the starting point of a conversation
• Align with stakeholders
• Change management
• Ethical and legal considerations

Questions?